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Computer Vision: Models, Learning, and Inference

Simon J. D. Prince

€ 92.34

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Description for Computer Vision: Models, Learning, and Inference Hardcover. A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms. Num Pages: 598 pages, 357 colour illus. 5 tables 201 exercises. BIC Classification: UYQV. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 255 x 187 x 32. Weight in Grams: 1428. Models, Learning, and Inference. 598 pages, 357 colour illus. 5 tables 201 exercises. A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms. Cateogry: (P) Professional & Vocational; (U) Tertiary Education (US: College). BIC Classification: UYQV. Dimension: 255 x 187 x 32. Weight: 1422.
This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build ... Read more

Product Details

Publisher

Cambridge University Press

Number of pages

598

Format

Hardback

Publication date

2012

Condition

New

Number of Pages

598

Place of Publication

Cambridge, United Kingdom

ISBN

9781107011793

SKU

V9781107011793

Shipping Time

Usually ships in 4 to 8 working days

Ref

99-4

About Simon J. D. Prince

Dr Simon J. D. Prince is a faculty member in the Department of Computer Science at University College London. He has taught courses on machine vision, image processing and advanced mathematical methods. He has a diverse background in biological and computing sciences and has published papers across the fields of computer vision, biometrics, psychology, physiology, medical imaging, computer graphics and ... Read more

Reviews for Computer Vision: Models, Learning, and Inference

'Computer vision and machine learning have married and this book is their child. It gives the machine learning fundamentals you need to participate in current computer vision research. It's really a beautiful book, showing everything clearly and intuitively. I had lots of 'aha!' moments as I read through the book. This is an important book for computer vision researchers and ... Read morestudents, and I look forward to teaching from it.' William T. Freeman, Massachusetts Institute of Technology 'With clarity and depth, this book introduces the mathematical foundations of probabilistic models for computer vision, all with well-motivated, concrete examples and applications. Most modern computer vision texts focus on visual tasks; Prince's beautiful new book is natural complement, focusing squarely on fundamental techniques, emphasizing models and associated methods for learning and inference. I think every serious student and researcher will find this book valuable. I've been using draft chapters of this remarkable book in my vision and learning courses for more than two years. It will remain a staple of mine for years to come.' David J. Fleet, University of Toronto 'This book addresses the fundamentals of how we make progress in this challenging and exciting field. I look forward to many decades with [this book] on my shelf, or indeed, I suspect, open on my desktop.' Andrew Fitzgibbon, from the Foreword 'Prince's magnum opus provides a fully probabilistic framework for understanding modern computer vision. With straightforward descriptions, insightful figures, example applications, exercises, background mathematics, and pseudocode, this book is self-contained and has all that is needed to explore this fascinating discipline.' Roberto Cipolla, University of Cambridge 'The author's goal, as stated in the preface, is to provide a book that focuses on the models involved, and I think the book has succeeded in doing that. I learned quite a bit and would recommend this text highly to the motivated, mathematically mature reader.' Jeffrey Putnam, Computing Reviews Computer vision and machine learning have gotten married and this book is their child. It gives the machine learning fundamentals you need to participate in current computer vision research. It's really a beautiful book, showing everything clearly and intuitively. I had lots of `aha!' moments as I read through the book. This is an important book for computer vision researchers and students, and I look forward to teaching from it. William T. Freeman, Massachusetts Institute of Technology With clarity and depth, this book introduces the mathematical foundations of probabilistic models for computer vision, all with well-motivated, concrete examples and applications. Most modern computer vision texts focus on visual tasks; Prince's beautiful new book is natural complement, focusing squarely on fundamental techniques, emphasizing models and associated methods for learning and inference. I think every serious student and researcher will find this book valuable. I've been using draft chapters of this remarkable book in my vision and learning courses for more than two years. It will remain a staple of mine for years to come. David J. Fleet, University of Toronto This book addresses the fundamentals of how we make progress in this challenging and exciting field. I look forward to many decades with [this book] on my shelf, or indeed, I suspect, open on my desktop. from the Foreword by Andrew Fitzgibbon Prince's magnum opus provides a fully probabilistic framework for understanding modern computer vision. With straightforward descriptions, insightful figures, example applications, exercises, background mathematics, and pseudocode, this book is self-contained and has all that is needed to explore this fascinating discipline. Roberto Cipolla, University of Cambridge The author's goal, as stated in the preface, is to provide a book that focuses on the models involved, and I think the book has succeeded in doing that. I learned quite a bit and would recommend this text highly to the motivated, mathematically mature reader. Jeffrey Putnam, Computing Reviews Show Less

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Computer Vision: Models, Learning, and Inference

Simon J. D. Prince

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